Much discussion again over at Language Log over a claim of the form “Language L has no word for concept C”. This time, it was the claim by Wade Davis (whose strange use of past tense indicates he has forgotten or is unaware that many Australian Aboriginal languages are still in use) that:

In not one of the hundreds of Aboriginal dialects and languages was there a word for time.”

The rebuttal of this claim by Mark Liberman was incisive and decisive. Davis was using this claim to support a more general argument: that traditional Australian Aboriginal cultures had different notions of and metaphors for time to those we mostly have in the modern Western world.

We in the contemporary educated West typically use a spatial metaphor for time, where the past is in one abstract place, the present in another non-overlapping abstract place, and the future in yet a third non-overlapping abstract place. In this construal of time, causal influence travels in one direction only: from the past to the present, and from the present to the future. Nothing in either the present or the future may influence the past, which is fixed and unchangeable. Events in the future may perhaps be considered to influence the present, depending on how much fluidity we allow the present to have. However, most of us would argue that it is not events in the future that influence events in the present, but our present perceptions of possible future events that influence events and actions in the present.

Modern Western Europeans typically think of the place that represents the past as being behind them, and the future ahead. People raised in Asian cultures often think of the abstract place that is the past as being below them (or above them), and the future above (or below). But all consider these abstract places to be non-overlapping, and even non-contiguous.

Traditional Australian Aboriginal cultures, as Davis argues, construe time very differently, and influences may flow in all directions. A better spatial metaphor for Aboriginal notions of time would be to consider a modern city, where there are many different types of transport and communications, each viewable as a network: rivers, canals, roads, bus-only road corridors, railways, underground rail tunnels, underground sewage or water drains, cycleways, footpaths, air-transport corridors, electricity networks, fixed-link telecommunications networks, wireless telecommunications networks, etc. A map of each of these networks could be created (and usually are) for specific audiences. A map of the city itself could then be formed from combining these separate maps, overlaid upon one another as layers in a stack. Each layer describes a separate aspect of reality, but the reality of the actual entire city is complex and more than merely the sum of these parts. Events or perceptions in one layer may influence events or perceptions in other layers, without any limitations on the directions of causality between layers.

Traditional Aboriginal notions of time are similar, with pasts, the present and futures all being construed as separate layers stacked over the same geographic space – in this case actual geographic country, not an abstract spatial representation of time. Each generation of people who have lived, or who will live, in the specific region (“country” in modern Aboriginal English) will have created a layer in the stack. Influence travels between the different layers in any and all directions, so events in the distant past or the distant future may influence events in the present, and events in the present may influence events in the past and the future.

Many religions – for example, Roman Catholicism, Hinduism, and African cosmologies – allow for such multi-directional causal influences via a non-material realm of saints or spirits, usually the souls of the dead, who may have power to guide the actions of the living in the light of the spirits’ better knowledge of the future. Causal influence can thus travel, via such spirit influences, from future to present. Similarly, the view of Quantum Mechanics of space-time as a single 4-dimensional manifold allows for influences across the dimension of time as well as those of space.

I am reminded of an experience I once witnessed where the only sensible explanation of a colleague’s passionate enthusiasm for a particular future course of action was his foreknowledge of the specific details of the outcome of that course of action. But these details he did not know and could not have known at the time of his enthusiasm, prior to the course of action being executed. In other words, only a causal influence from future to present provided a sensible explanation for this enthusiasm, and this explanation only became evident as the future turned into the present, and the details of the outcome emerged. Until that point, he could not justify or explain his passionate enthusiasm, which seemed to be a form of madness, even to him. Contemporary Western cosmology does not provide such time-reversing explanations, but many other cultures do; and current theories of quantum entanglement also seem to.

Contemporary westerners, particularly those trained in western science, have a hard time understanding such alternative cosmologies, in my experience. I have posted before about the difficulties most westerners have, for instance, in understanding Taoist/Zen notions of synchronicity of events, which westerners typically mis-construe as random chance.

Steven Poole has an interesting debunking of some prominent cyber-gurus in The New Statesman, “Invasion of the Cyber Hustlers”, here. In it, he writes:

However, it doesn’t matter if cyber-hustlers are wrong about the present, because their brand value is more as wireless Nostradamuses. The cyber-maniac ideates a perfect cyber-future and affirms at the top of his voice that it has already arrived, or is so vague about the date of its realisation that he could never possibly be refuted. The title of a recent Ted talk by Shirky is a beautiful example of such unfalsifiable cyber-augury: “How the Internet Will (One Day) Transform Government.”

I was reminded of this 1707 statement by French Prophet John Lacy I had earlier quoted:

They know not what to make of the Words, little time, speedily, shortly, suddenly, soon. They would have me define the Time, in the Prophecies of my ancient Servants. Yet those Predictions carried in them my authority, and were fulfilled soon enough, for those that suffered under them . . . I have seen it best, not to assign the punctual Times, by their Definition among Men; that I might keep Men always in their due distance, and reverential Fear of invading what I reserve, in secret, to myself . . . The Tower-Guns are the Tormenta e Turre aethera, with which this City I have declared should be battered . . . I have not yet given a Key to Time in this Revelation.”

Lacy was explaining to his followers among the millenarian French Huguenot sect in Britain why his prophecies had not yet been fulfilled. (Source details here.)

Alan Greenspan, then Chairman of the US Federal Reserve Bank System, speaking in January 2004, discussed the failure of traditional methods in econometrics to provide adequate guidance to monetary policy decision-makers. His words included:

Given our inevitably incomplete knowledge about key structural aspects of an ever-changing economy and the sometimes asymmetric costs or benefits of particular outcomes, a central bank needs to consider not only the most likely future path for the economy but also the distribution of possible outcomes about that path. The decisionmakers then need to reach a judgment about the probabilities, costs, and benefits of the various possible outcomes under alternative choices for policy.”

The product of a low-probability event and a potentially severe outcome was judged a more serious threat to economic performance than the higher inflation that might ensue in the more probable scenario.”

It has always struck me that Karl Marx’s prediction that capitalism would be eclipsed by socialism and then by communism was a self-denying prophecy: because he made this prediction, and because of the widespread popularity of his (and other socialists’) ideas, politicians and businessmen were moved to act in ways which allowed capitalism to adapt, rather than to die. It seems that the end of communism may have been partly due to similar reflective-system effects.

In her book, Stasiland: True Stories from Behind the Berlin Wall, Anna Funder writes the following about the opposition to the Socialist Unity Party (SED) in the former German Democratic Republic (the DDR):

I once saw a note on a Stasi file from early 1989 that I would never forget. In it a young lieutenant alerted his superiors to the fact that there were so many informers in church opposition groups at demonstrations that they were making these groups appear stronger than they really were. In one of the most beautiful ironies I have ever seen, he dutifully noted that it appeared that, by having swelled the ranks of the opposition, the Stasi was giving the people heart to keep demonstrating against them. (pp. 197-198)

NOTE: A comment about the processes which led to the end of communism in the USSR is contained in this post.

What are models for? Most developers and users of models, in my experience, seem to assume the answer to this question is obvious and thus never raise it. In fact, modeling has many potential purposes, and some of these conflict with one another. Some of the criticisms made of particular models arise from mis-understandings or mis-perceptions of the purposes of those models, and the modeling activities which led to them.

Liking cladistics as I do, I thought it useful to list all the potential purposes of models and modeling. The only discussion that considers this topic that I know is a brief discussion by game theorist Ariel Rubinstein in an appendix to a book on modeling rational behaviour (Rubinstein 1998). Rubinstein considers several alternative purposes for economic modeling, but ignores many others. My list is as follows (to be expanded and annotated in due course):

1. To better understand some real phenomena or existing system. This is perhaps the most commonly perceived purpose of modeling, in the sciences and the social sciences.

2. To predict (some properties of) some real phenomena or existing system. A model aiming to predict some domain may be successful without aiding our understanding of the domain at all. Isaac Newton’s model of the motion of planets, for example, was predictive but not explanatory. I understand that physicist David Deutsch argues that predictive ability is not an end of scientific modeling but a means, since it is how we assess and compare alternative models of the same phenomena. This is wrong on both counts: prediction IS an end of much modeling activity (especially in business strategy and public policy domains), and it not the only means we use to assess models. Indeed, for many modeling activities, calibration and prediction are problematic, and so predictive capability may not even be possible as a form of model assessment.

3. To manage or control (some properties of) some real phenomena or existing system.

4. To better understand a model of some real phenomena or existing system. Arguably, most of economic theorizing and modeling falls into this category, and Rubinstein’s preferred purpose is this type. Macro-economic models, if they are calibrated at all, are calibrated against artificial, human-defined, variables such as employment, GDP and inflation, variables which may themselves bear a tenuous and dynamic relationship to any underlying economic reality. Micro-economic models, if they are calibrated at all, are often calibrated with stylized facts, abstractions and simplifications of reality which economists have come to regard as representative of the domain in question. In other words, economic models are not not usually calibrated against reality directly, but against other models of reality. Similarly, large parts of contemporary mathematical physics (such as string theory and brane theory) have no access to any physical phenomena other than via the mathematical model itself: our only means of apprehension of vibrating strings in inaccessible dimensions beyond the four we live in, for instance, is through the mathematics of string theory. In this light, it seems nonsense to talk about the effectiveness, reasonable or otherwise, of mathematics in modeling reality, since how we could tell?

5. To predict (some properties of) a model of some real phenomena or existing system.

6. To better understand, predict or manage some intended (not-yet-existing) artificial system, so to guide its design and development. Understanding a system that does not yet exist is qualitatively different to understanding an existing domain or system, because the possibility of calibration is often absent and because the model may act to define the limits and possibilities of subsequent design actions on the artificial system. The use of speech act theory (a model of natural human language) for the design of artificial machine-to-machine languages, or the use of economic game theory (a mathematical model of a stylized conceptual model of particular micro-economic realities) for the design of online auction sites are examples here. The modeling activity can even be performative, helping to create the reality it may purport to describe, as in the case of the Black-Scholes model of options pricing.

7. To provide a locus for discussion between relevant stakeholders in some business or public policy domain. Most large-scale business planning models have this purpose within companies, particularly when multiple partners are involved. Likewise, models of major public policy issues, such as epidemics, have this function. In many complex domains, such as those in public health, models provide a means to tame and domesticate the complexity of the domain. This helps stakeholders to jointly consider concepts, data, dynamics, policy options, and assessment of potential consequences of policy options, all of which may need to be socially constructed.

8. To provide a means for identification, articulation and potentially resolution of trade-offs and their consequences in some business or public policy domain. This is the case, for example, with models of public health risk assessment of chemicals or new products by environmental protection agencies, and models of epidemics deployed by government health authorities.

9. To enable rigorous and justified thinking about the assumptions and their relationships to one another in modeling some domain. Business planning models usually serve this purpose. They may be used to inform actions, both to eliminate or mitigate negative consequences and to enhance positive consequences, as in retroflexive decision making.

10. To enable a means of assessment of managerial competencies of the people undertaking the modeling activity. Investors in start-ups know that the business plans of the company founders are likely to be out of date very quickly. The function of such business plans is not to model reality accurately, but to force rigorous thinking about the domain, and to provide a means by which potential investors can challenge the assumptions and thinking of management as way of probing the managerial competence of those managers. Business planning can thus be seen to be a form of epideictic argument, where arguments are assessed on their form rather than their content, as I have argued here.

11. As a means of play, to enable the exercise of human intelligence, ingenuity and creativity, in developing and exploring the properties of models themselves. This purpose is true of that human activity known as doing pure mathematics, and perhaps of most of that academic activity known as doing mathematical economics. As I have argued before, mathematical economics is closer to theology than to the modeling undertaken in the natural sciences. I see nothing wrong with this being a purpose of modeling, although it would be nice if academic economists were honest enough to admit that their use of public funds was primarily in pursuit of private pleasures, and any wider social benefits from their modeling activities were incidental.

POSTSCRIPT (Added 2011-06-17): I have just seen Joshua Epstein’s 2008 discussion of the purposes of modeling in science and social science. Epstein lists 17 reasons to build explicit models (in his words, although I have added the label “0” to his first reason):

In previous posts (eg, here and here), I have talked about the difficulty of assessing the intentions of others, whether for marketing or for computer network design or for national security. The standard English phrase speaks of “putting ourselves in the other person’s shoes”. But this is usually not sufficient: we have to put them into their shoes, with their beliefs, their history, their desires, and their constraints, not ourselves, in order to understand their goals and intentions, and to anticipate their likely strategies and actions. In a fine political thriller by Henry Porter, I come across this statement (page 220):

‘Motive is always difficult to read,’ he replied. ‘We make a rational assumption about someone’s behaviour based on what we would, or would not, do in the same circumstances, ignoring the otherness of the other. We consider only influences that make us what we are and impose those beliefs on them. It is the classic mistake of intelligence analysis.’ “

An orrery is a machine for predicting the movements of heavenly bodies. The oldest known orrery is the Antikythera Mechanism, created in Greece around 2100 years ago, and rediscovered in 1901 in a shipwreck near the island of Antikythera (hence its name). The high-quality and precision nature of its components would indicate that this device was not unique, since the making of high-quality mechanical components is not trivial, and is not usually achieved with just one attempt (something Charles Babbage found, and which delayed his development of computing machinery immensely).

It took until 2006 and the development of x-ray tomography for a plausible theory of the purpose and operations of the Antikythera Mechanism to be proposed (Freeth et al. 2006). The machine was said to be a physical examplification of late Greek theories of cosmology, in particular the idea that the motion of a heavenly body could be modeled by an epicycle – ie, a body traveling around a circle, which is itself moving around some second circle. This model provided an explanation for the fact that many heavenly bodies appear to move at different speeds at different times of the year, and sometimes even (appear to) move backwards.

There have been two recent developments: One is the re-creation of the machine (or, rather, an interpretation of it) using lego components.

The second has arisen from a more careful examination of the details of the mechanism. According to Marchant (2010), some people now believe that the mechanism examplifies Babylonian, rather than Greek, cosmology. Babylonian astronomers modeled the movements of heavenly bodies by assuming each body traveled along just one circle, but at two different speeds: movement in one period of the year being faster than during the other part of the year.

If this second interpretation of the Antikythera Mechanism is correct, then perhaps it was the mechanism itself (or others like it) which gave late Greek astronomers the idea for an epicycle model. In support of this view is the fact that, apparently, gearing mechanisms and the epicycle model both appeared around the same time, with gears perhaps a little earlier. So late Greek cosmology (and perhaps late geometry) may have arisen in response to, or at least alongside, practical developments and physical models. New ideas in computing typically follow the same trajectory – first they exist in real, human-engineered, systems; then, we develop a formal, mathematical theory of them. Programmable machines, for instance, were invented in the textile industry in the first decade of the 19th century (eg, the Jacquard Loom), but a mathematical theory of programming did not appear until the 1960s. Likewise, we have had a fully-functioning, scalable, global network enabling multiple, asynchronous, parallel, sequential and interleaved interactions since Arpanet four decades ago, but we still lack a thorough mathematical theory of interaction.

And what have the Babylonians ever done for us? Apart from giving us our units for measuring of time (divided into 60) and of angles (into 360 degrees)?

Norm at Normblog has a post defending theology as a legitimate area of academic inquiry, after an attack on theology by Oliver Kamm. (Since OK’s post is behind a paywall, I have not read it, so my comments here may be awry with respect to that post.) Norm argues, very correctly, that it is legitimate for theology, considered as a branch of philosophy to, inter alia, reflect on the properties of entities whose existence has not yet been proven. In strong support of Norm, let me add: Not just in philosophy!

In business strategy, good decision-making requires consideration of the consequences of potential actions, which in turn requires the consideration of the potential actions of other actors and stakeholders in response to the first set of actions. These actors may include entities whose existence is not yet known or even suspected, for example, future competitors to a product whose launch creates a new product category. Why, there’s even a whole branch of strategy analysis, devoted to scenario planning, a discipline that began in the military analysis of alternative post-nuclear worlds, and whose very essence involves the creation of imagined futures (for forecasting and prognosis) and/or imagined pasts (for diagnosis and analysis). Every good air-crash investigation, medical diagnosis, and police homicide investigation, for instance, involves the creation of imagined alternative pasts, and often the creation of imaginary entities in those imagined pasts, whose fictional attributes we may explore at length. Arguably, in one widespread view of the philosophy of mathematics, pure mathematicians do nothing but explore the attributes of entities without material existence.

And not just in business, medicine, the military, and the professions. In computer software engineering, no new software system development is complete without due and rigorous consideration of the likely actions of users or other actors with and on the system, for example. Users and actors here include those who are the intended target users of the system, as well as malevolent or whimsical or poorly-behaved or bug-ridden others, both human and virtual, not all of whom may even exist when the system is first developed or put into production. If creative articulation and manipulation of imaginary futures (possible or impossible) is to be outlawed, not only would we have no literary fiction or much poetry, we’d also have few working software systems either.

I have just learnt of the death last month of Sol Encel, Emeritus Professor of Sociology at the University of New South Wales, and a leading Australian sociologist, scenario planner, and futures thinker. I took a course on futurology with him two decades ago, and it was one of the most interesting courses I ever studied. This was not due to Encel himself, at least not directly, who appeared in human form only at the first lecture.

He told us he was a very busy and important man, and would certainly not have the time to spare to attend any of the subsequent lectures in the course. Instead, he had arranged a series of guest lectures for us, on a variety of topics related to futures studies, futurology, and forecasting. Because he was genuinely important, his professional network was immense and impressive, and so the guest speakers he had invited were a diverse group of prominent people, from different industries, academic disciplines, professions, politics and organizations, each with interesting perspectives or experiences on the topic of futures and prognosis. The talks they gave were absolutely fascinating.

To accommodate the guest speakers, the lectures were held in the early evening, after normal working hours. Because of this unusual timing, and because the course assessment comprised only an essay, student attendance at the lectures soon fell sharply. Often I turned up to find I was the only student present. These small classes presented superb opportunities to meet and talk with the guest speakers, conversations that usually adjourned to a cafe or a bar nearby. I learnt a great deal about the subject of forecasting, futures, strategic planning, and prognosis, particularly in real organizations with real stakeholders, from these interactions. Since he chose these guests, I thus sincerely count Sol Encel as one of the important influences on my thinking about futures.

Here, in a tribute from the Australian Broadcasting Commission, is a radio broadcast Encel made in 1981 about Andrei Sakharov. It is interesting that there appears to have been speculation in the West then has to how the so-called father of the Soviet nuclear bomb could have become a supporter of dissidents. This question worried, too, the KGB, whose answer was one Vadim Delone, poet. And here, almost a month after Solomon Encel’s death, is his obituary in the Sydney Morning Herald. One wonders why this took so long to be published.